A binary options trading system based on a genetic algorithm questions


The BH strategy will lose on these occasions, while the generated trading rule can help traders foresee a decline in price and reduce losses. Compared with normal distribution, there are more values located in the tails of the distribution in our results. Category 4 periods 4, 13, 19, 20, and When the price is relatively stable, an investment decision should be made immediately as long as the two moving averages cross. The fitness value calculation, selection, crossover, and mutation of individuals are implemented using the GA toolbox of Sheffield in the Matlab platform.

Therefore, we designed the two rules in our initial trading rules. The AMA can change the weights of the current price according to the volatility in the last several days. If the long average price is lower than the short average price, a trader will take a long position. Figure 5 shows that TPMA is used 31 times in the 60 independent experiments in periods 2, 3, and 9 Category 1. In these periods, moving average indicators cannot find profit opportunities because the volatility is too small.

In these periods, generated trading rules not only help traders obtain returns but also help them to realize excess returns. If the long average price is lower than the short average price, a trader will take a long position. Only in half of the experiments, is between 70 days and days.

The fitness of every individual is calculated in the evaluation step. Static moving average trading rules with fixed period lengths cannot adapt to complex fluctuations of price in different periods. Different from the overall proportion, TPMA is the most popular calculation method when price falls during the period and experienced significant fluctuations. We conclude that the genetic algorithms identify better technical rules that allow traders to actualize profits from their investments. In this paper, we search best trading rules according to the return rate of each one without regard to asset conditions and open interest, which proves to be the greatest limitation of the study.

However, they cannot generate more returns than the BH strategy. However, the selection of best moving average calculation method is affected by price trends. According to existing literature, the long period is generally between 20 and days very few studies use periods longer than days [ 3839 ], and the short period is generally no longer than 60 days. Test the best trading rule as identified by the above program. Randomly create an initial population of 20 moving average trading rules.

In period 21, the BH strategy yields negative returns. The following prices are used to select the best generated trading rule from all generations, and the last daily prices are used to determine whether the generated rule can acquire excess returns. Among the six moving average methods, the AMA and TMA are the most popular among the generated trading rules as these two methods have the ability to adapt to the price trends. The authors used daily crude oil prices of NYMEX futures from to to evaluate and select moving average rules.

Conversely, taking a short position may also be a good rule. The fitness of a trading rule is calculated according to the profit it can make in the crude oil futures market. When prices fluctuate, such as in periods 1, 2, 7, 8, 13, 19, and 20, then not opening positions until one average price exceeds another by at least one standard deviation is the best option. According to existing literature, the long period is generally between 20 and days very few studies use periods longer than days [ 3839 ], and the short period is generally no longer than 60 days. Select the rule with the highest fitness value and evaluate it for the selection period to obtain its return rate.

In this paper, we use genetic algorithms to determine appropriate lengths of the moving average period. With a probability of 0. A common feature of these three periods in Category 1 is that the crude oil prices fell during the test period and experienced significant fluctuations.

Crude oil futures market is a crucial part of energy finance within the scope of the global energy market. The generated moving average trading rules demonstrate outstanding performance when the crude oil futures price falls with significant fluctuations. However, genetic algorithms cannot guarantee access to additional revenue in every period as they are only useful in acquiring a binary options trading system based on a genetic algorithm questions returns in special situations. For a better understanding, we divide the 21 periods into 4 categories according to the results see the last column of Table 2. The figure presents a typical fat tail characteristic with a kurtosis of 2.

Table of Contents Alerts. Accordingly, all the recombination rules will be mutated with a probability of 0. Only in half of the experiments, is between 70 days and days. While there are no significant changes in the prices level in these periods, the prices are in volatile states throughout the five periods.